(1) Field of the Invention
The present invention relates to a polishing technique and, more particularly, to a shape prediction simulator technique for predicting the shape of a polished surface formed by chemical mechanical polishing (CMP).
(2) Description of Related Art
CMP processes are being put to greater use as a flattening technique and a wiring forming technique for semiconductor devices (see Japanese Patent Laid-Open No. 2003-282495). Also, shape prediction simulator techniques for predicting a flattened shape or a wiring forming shape in a CMP process are being established in recent years.
The operation of a CMP shape prediction simulator will be described as an art related to the present invention.
Referring to FIG. 1, conditions for computation of the pattern density in a semiconductor device are set (step S10). In this pattern density computation condition setting, an analysis area on which a shape prediction is to be made is designated on the basis of layout data (a design drawing) which is information on the design of the semiconductor device, and a computation resolution is determined. The whole or part of the design drawing can be designated as the analysis area.
Subsequently, the pattern density in the designated analysis area is extracted. For this pattern density extraction processing, high-efficiency computation processing can be performed by using distributed processing, because there is no need to consider the influence from adjacent device patterns. In the example shown in FIG. 1, local processing in step S11 or distributed processing in step S12 is performed as pattern density extraction processing.
In local processing, as shown in FIG. 2, one processor (central processing unit (CPU)) performs processing for computing the pattern density in the entire objective region on which computation processing is to be performed. The CPU divides the entire objective region into a plurality of regions in grid form and computes the pattern density in each divided region. For a grid division, the grid size is determined by the computation resolution.
On the other hand, in distributed processing, as shown in FIG. 3, the region on which computation processing is to be performed is divided into four regions, and the divided regions are respectively assigned to four processors (CPUs). Each CPU divides the assigned region into a plurality of regions in grid form and computes the pattern density in each divided region.
After pattern density computation processing in step S11 or S12 is performed, process conditions are set. In this process condition setting, process conditions such as a model selection and a processing time for making a shape prediction are set (step S13). Shape prediction computation is then performed (step S14) according to the computation resolution set in step S10, the pattern densities extracted in step S11 or S12 and the process conditions set in step S13, and hot spot analysis is thereafter performed (step S15).
The inventor of the present invention recognized a problem described below with the CMP shape prediction simulator described above as a related art.
In a CMP process, polishing is performed by using physical contact among a pattern of a device, a polishing pad and slurry (abrasive grains) and, therefore, the shape of the polished device depends largely on the device pattern density. The shape formed by polishing is also influenced by an adjacent device pattern.
FIG. 4 shows the results of comparison between a case where shape prediction computation processing is performed by one CPU (local processing) and a case where shape prediction computation processing is performed by four CPUs (distributed processing). In a section on the left-hand side of FIG. 4, an example of local processing and an example of distributed processing are shown together with objective figures on which the processing is performed. On the right-hand side, a graph is shown which shows the results of shape prediction by local processing and distributed processing and values actually measured. The objective figure on which local processing is performed and the objective figure on which distributed processing is performed are identical to each other. In the graph, the ordinate represents the height of a polished portion and the abscissa represents the range of analysis (scan length). In the graph, the thickest line indicates variations in height of a polished portion in a-a′ of the objective figure on which local processing is performed, and the second thickest line indicates variations in height of a polished portion in b-b′ of the objective figure on which distributed processing is performed. The thinnest line indicates the actually measured values of the actually polished portion.
The computation processing time in the case of local processing is 6720 seconds, while the computation processing time in the case of distributed processing is 1700 seconds. Speed-up by about four times can be achieved by applying distributed processing to shape prediction computation processing. However, this distributed processing is processing in which computation is performed by simply dividing an analysis area into four, such that shape prediction is not performed by considering the influence of device pattern elements extending across the figure boundaries and the influence of device pattern elements in the vicinity of the boundaries. As a result, when distributed processing is used, the difference between the prediction result and the actually measured value is increased in the vicinity of the figure boundary portion (b-b′) to considerably reduce the shape prediction accuracy, as shown in the graph of FIG. 4.
For this reason, local processing is applied to shape prediction computation processing in the above-described CMP shape prediction simulator. Therefore, a considerably long time is required for shape prediction computation processing, depending on conditions including the analysis area and the computation resolution.